Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@PhdThesis{Das:thesis,

• author = "Abhishek Das",
• title = "Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms",
• school = "Department of Civil, Environmental, and Construction Engineering (CECE) of the University of Central Florida",
• year = "2009",
• address = "Orlando, USA",
• month = "13 " # oct,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cecs.ucf.edu/graddefense/pdf/10",
• URL = "http://purl.fcla.edu/fcla/etd/CFE0002928",
• size = "212 pages",
• abstract = "Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; unforgiving weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributory factors for crashes and injury severity. Advances in computational methods render the use of modern statistical and machine learning algorithms. Even though most of the contributing factors are known a-priori, advanced methods unearth changing trends. Heuristic evolutionary processes such as linear genetic programming; sophisticated data mining methods like conditional inference tree; and mathematical treatments in the form of sensitivity analyses outline the major contributions in this research. Application of traditional statistical methods like simultaneous ordered probit models, identification and resolution of crash data problems are also key aspects of this study. In order to eliminate the use of unrealistic uniform intersection influence radius of 250 ft, heuristic rules were developed for assigning crashes to roadway segments, junctions with traffic lights intersection and access points using parameters, such as 'site location' and 'traffic control'. Use of Conditional Inference Forest instead of Classification and Regression Tree to identify variables of significance for injury severity analysis removed the bias towards the selection of continuous variable or variables with large number of categories. Concepts of evolutionary biology like crossover and mutation were implemented to develop models for classification and regression analyses based on the highest hit rate and minimum error rate, respectively. Annual daily traffic; friction coefficient of pavements; on-street parking; curbed medians; surface and shoulder widths; alcohol / drug usage are some of the significant factors that played a role in both the crash occurrence and injury severities. Relative sensitivity analyses were used to identify the effect of continuous variables on the variation of crash counts. This study improved the understanding of the significant factors that could play an important role in designing better safety countermeasures on multi lane highways, and hence enhance their safety by reducing the frequency of crashes and severity of injuries.",
• notes = "Supervisor Mohamed A. Abdel-Aty",

}

Genetic Programming entries for Abhishek Das

Citations